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Using Minion™ to Characterize Dog Skin Microbiota Through Full-Length 16S Rrna Gene Sequencing Approach

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Using Minion™ to Characterize Dog Skin Microbiota Through Full-Length 16S Rrna Gene Sequencing Approach bioRxiv preprint doi: https://doi.org/10.1101/167015; this version posted July 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Using MinION™ to characterize dog skin microbiota through full-length 16S rRNA gene sequencing approach Anna Cuscó1,2, Joaquim Viñes2, Sara D’Andreano1,2, Francesca Riva2, Joaquim Casellas2, Armand Sánchez2 and Olga Francino2 1 Vetgenomics, Ed Eureka, Parc de Recerca UAB, Barcelona, Spain 2 Molecular Genetics Veterinary Service (SVGM), Veterinary School, Universitat Autònoma de Barcelona, Barcelona, Spain Corresponding author: Anna Cuscó, [email protected] Keywords: MinION, nanopore, 3rd generation sequencing, microbiota, microbiome, 16S rRNA, dog, canine, skin, inner pinna 1 bioRxiv preprint doi: https://doi.org/10.1101/167015; this version posted July 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Abstract The most common strategy to assess microbiota is sequencing specific hypervariable regions of 16S rRNA gene using 2nd generation platforms (such as MiSeq or Ion Torrent PGM). Despite obtaining high-quality reads, many sequences fail to be classified at the genus or species levels due to their short length. This pitfall can be overcome sequencing the full-length 16S rRNA gene (1,500bp) by 3rd generation sequencers. We aimed to assess the performance of nanopore sequencing using MinION™ on characterizing microbiota complex samples. First set-up step was performed using a staggered mock community (HM- 783D). Then, we sequenced a pool of several dog skin microbiota samples previously sequenced by Ion Torrent PGM. Sequences obtained for full-length 16S rRNA with degenerated primers retrieved increased richness estimates at high taxonomic level (Bacteria and Archaea) that were missed with short-reads. Besides, we were able to obtain taxonomic assignments down to species level, although it was not always feasible due to: i) incomplete database; ii) primer set chosen; iii) low taxonomic resolution of 16S rRNA gene within some genera; and/or iv) sequencing errors. Nanopore sequencing of the full-length 16S rRNA gene using MinION™ with 1D sequencing kit allowed us inferring microbiota composition of a complex microbial community to lower taxonomic levels than short-reads from 2nd generation sequencers. Introduction Bacteria, fungi, viruses and archaea are the main microorganisms constituting the microbiota, which is defined as the microbial communities inhabiting a specific environment (1). In humans, many efforts have been made to characterize the different body site ecosystems and their associated microbial communities, mainly at bacterial level (2,3), which are the most abundant microorganisms on the human-associated microbiota (4,5). Studying host-associated microbiota has provided many insights on health and diseases for many different body sites (6,7). In human skin, alterations on skin microbiota have been associated to numerous cutaneous diseases, such as acne vulgaris (8,9), psoriasis (10–12), or atopic dermatitis (13–17). Not only humans, but also dogs presented, for example, altered microbiota states during atopic dermatitis disease (18–20). The most common strategy to assess bacterial microbiota is amplifying and sequencing specific regions of 16S rRNA gene using 2nd generation massive sequencing technologies (for a review see (21)). This bacterial marker gene is ubiquitously found in bacteria, and has nine hypervariable regions (V1-V9) that can be used to infer taxonomy (22). The ability to classify sequences to the genus or species level is a function of read length, sample type, the reference database (23), and the quality of the sequence. High-quality short-reads obtained from 2nd generation sequencers (250-350 bp) bias and limit the taxonomic resolution of this gene. The most common region amplified with Illumina MiSeq or Ion Torrent PGM™ for bacterial taxonomic classification is V4, but this region fails to amplify some significant species for skin microbiota studies, such as Propionibacterium acnes. So, when performing a skin microbiota study the preferred choice is V1- V2 regions, although they lack sensitivity for the genus Bifidobacterium and poorly amplify the phylum Verrucomicrobia (21). On the other hand, near full-length 16S rRNA gene sequences are required for accurate richness estimations especially at higher taxa (24), which are necessary on microbiota studies. Besides, full-length reference sequences are needed for performing phylogenetic analyses or designing lineage specific primers (23), especially in species different to human or mouse, in which previous 2 bioRxiv preprint doi: https://doi.org/10.1101/167015; this version posted July 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. metagenomics approaches deciphered the richness of bacterial species in the great and different variety of microbiome samples analyzed. With the launching of 3rd generation single-molecule technology sequencers, these short-length associated problems can be overcome by sequencing the full or almost full-length of 16S rRNA gene with different sets of universal primers (25). Results for full-length 16S rRNA gene have been reported for Pacific Biosciences (PacBio) platform (23,26–30). Schloss and collaborators reported the possibility of generating near full-length 16S rRNA gene sequences with error rates slightly higher, but comparable to the 2nd generation platforms (0.03%) (23). The primary limitation on the PacBio platform is the accessibility to the sequencers and the cost of generating the data. MinIONTM sequencer of Oxford Nanopore Technologies (ONT) (https://nanoporetech.com) is a 3rd generation sequencer that is portable, affordable with a small budget and offers long-read output (only limited by DNA extraction protocol). Besides, it can provide a rapid real-time and on-demand analysis very useful on clinical applications. Several studies targeting the full 16S rRNA gene have already been performed using MinIONTM to: i) identify pure bacterial culture (31); ii) characterize artificial and already- characterized bacterial communities (mock community) (32–34); and to iii) characterize complex microbiota samples, from mouse gut (35), wastewater (31) and pleural effusion from a patient with empyema (34). Here we aim to assess the potential of Nanopore sequencing in complex microbiota samples using the full- length 16S rRNA (1,500bp). First set-up step is performed using a staggered mock community (HM- 783D). Then, we sequenced a pool of several skin microbiota samples previously sequenced by Ion Torrent PGM™. Material and methods Samples and DNA extraction As simple microbial community, we used a Microbial Mock Community HM-783D kindly donated by BEI resources (http://www.beiresources.org) that contained genomic DNA from 20 bacterial strains with staggered ribosomal RNA operon counts (1,000 to 1,000,000 copies per organism per µL). This mock community allowed us to perform the MinIONTM sequencing and analysis protocol set-up. As complex microbial community, we used a sample pool from inner pinna skin microbiota of healthy dogs, which had been previously characterized using Ion Torrent PGM™. Skin microbiota samples were collected using Sterile Catch-All™ Sample Collection Swabs (Epicentre Biotechnologies) soaked in sterile SCF-1 solution (50 mM Tris buffer (pH = 8), 1 mM EDTA, and 0.5% Tween-20). Bacterial DNA was extracted from the swabs using the PowerSoil™ DNA isolation kit (MO BIO) (for further details on sample collection and DNA extraction see (36)). MinION™: PCR amplification and barcoding To prepare the DNA and the library we followed the Oxford Nanopore protocol 1D PCR barcoding amplicons (SQK-LSK108), however we used the Phusion Taq polymerase rather than the LongAmp Taq recommended in this protocol. Specifically, we amplified ~1,500bp fragments of the full 16S rRNA gene. 3 bioRxiv preprint doi: https://doi.org/10.1101/167015; this version posted July 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Bacterial DNA was amplified using a nested PCR with a first round to add the 16S rRNA gene primer sets and a second round to add the barcodes. In this study we used two sets of 16S universal primers. On one hand, primer set 27F-1391R (also named S-D-Bact-0008-c-S-20 and S-D-Bact-1391-a-A-17 (37)) amplified V1-V8 hypervariable regions of 16S rRNA gene. On the other hand, primer set 27F-1492R (also named S-D-Bact-0008-c-S-20 and S-D-Bact-1492-a-A-22 (37)) amplified V1-V9 hypervariable regions of 16S rRNA gene. These two sets of universal primers are the most commonly used when assessing full- length 16S rRNA gene, because they have shown a really low non-coverage rate, even at phylum level (38). The primers used in this study are listed in Table 1 and contain some ambiguous bases previously described to make the primers more universal (25). Table 1. Primer sequences and hypervariable regions (HVR) targeted for full-length 16S rRNA gene amplification and sequencing. Short Complete name HVR Sequence (5' 3') Melting T name S-D-Bact-0008-c-S-20 27F V1 AGRGTTYGATYMTGGCTCAG 54.4 ºC S-D-Bact-1391-a-A-17 1391R V8 GACGGGCGGTGWGTRCA 59.5 ºC S-D-Bact-1492-a-A-22 1492R V9 TACCTTGTTAYGACTT 41.6 ºC We will distinguish among primer sets used referring to the hypervariable regions they are amplifying, so: 27F-1391R will be V1-V8; and 27F-1492R will be V1-V9.
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